부산대학교 도서관

With the coming 5G era, eXtended Reality (XR) is expected to grow at a rapid place for multimedia services. In recent years, LiDAR technology for XR has potential to become the prominent method for 3D scanning in mobile devices. However, performing heavy computational tasks (such as 3D graphics processing and executing deep neural networks (DNN) models) on XR devices and transferring XR data to another devices are some of the major challenges. This is because of the on-device limited processing power, network bandwidth availability and low battery capacity, etc., to transmit XR data to the nearby edge devices. These arise the requirements of a suitable intelligent offloading mechanisms, compression techniques, and performing DNN inference layers partition among various devices. To address the above issues, we propose eXtended Reality Split Offloading (XrSplitOffload) mechanism. The XrSplitOffload consists of three parts: Firstly, device capability based intelligent offloading (DCIO) methodology is proposed which intelligently distributes XR data, decides when to perform low/partial/high offloading. Secondly, LiDAR mesh data creations and suitable compression techniques are illustrated. Finally, we propose a compressed optimal split compute (COSC) algorithm which computes split computing on compressed DNN for XR 3D mesh applications. We conduct extensive experiments to compute the performance of proposed mechanisms with respect to the state of art.